Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles
Ni-Ching Lin, Yu-Chieh Hsiao, Yi-Wei Huang, Ching-Tung Hung, Tzu-Kuan Chuang, Pin-Wei Chen, Jui-Te Huang, Chao-Chun Hsu
Abstract
Duckiepond is an education and research development environment that includes software systems, educational materials, and of a fleet of autonomous surface vehicles Duckieboat. Duckieboats are designed to be easily reproducible with parts from a 3D printer and other commercially available parts, with flexible software that leverages several open source packages. The Duckiepond environment is modeled after Duckietown and AI Driving Olympics environments: Duckieboats rely only on one monocular camera, IMU, and GPS, and perform all ML processing using onboard embedded computers. Duckiepond coordinates commonly used middlewares (ROS and MOOS) and containerized software packages in Docker, making it easy to deploy. The combination of learning-based methods together with classic methods enables important maritime missions: track and trail, navigation, and coordinate among Duckieboats to avoid collisions. Duckieboats have been operating in a man-made lake, reservoir and river environments. All software, hardware, and educational materials are openly available (https://robotx-nctu.github.io/duckiepond), with the goal of supporting research and education communities across related domains.
BibTeX
@inproceedings{iros2019_duckiepondanopen,
title = {Duckiepond: An Open Education and Research Environment for a Fleet of Autonomous Maritime Vehicles},
author = {Ni-Ching Lin and Yu-Chieh Hsiao and Yi-Wei Huang and Ching-Tung Hung and Tzu-Kuan Chuang and Pin-Wei Chen and Jui-Te Huang and Chao-Chun Hsu and Andrea Censi and Michael Benjamin and Chi-Fang Chen and Hsueh-Cheng Wang},
booktitle = {IROS 2019},
year = {2019}
}